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The rapid advancement of deep learning has revolutionized numerous fields, from computer vision to natural language processing. However, the effectiveness of deep learning models heavily relies on the choice of their architectures. Traditionally, designing these architectures has been a labor-intensive and expert-driven process. To address this challenge, Gradient-based Neural Architecture Search (NAS) has emerged as a promising solution, aiming to automate the architecture design process.

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Gradient-Based Neural Architecture Search

  • 章节

  • 发表日期：

    2025年

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  • M. Arif Wani,
  • Bisma Sultan,
  • Sarwat Ali,
  • Mukhtar Ahmad Sofi

摘要

The rapid advancement of deep learning has revolutionized numerous fields, from computer vision to natural language processing. However, the effectiveness of deep learning models heavily relies on the choice of their architectures. Traditionally, designing these architectures has been a labor-intensive and expert-driven process. To address this challenge, Gradient-based Neural Architecture Search (NAS) has emerged as a promising solution, aiming to automate the architecture design process.